Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4960752 | Procedia Computer Science | 2017 | 6 Pages |
Abstract
In this paper, we introduce an unsupervised stochastic statistical approach for ranking key-phrases, and identifying the salient sentences within a single document for generic extractive summaries. In particular, we propose a method to perceive the salient information of a text unit which is related to the corresponding title and its leverage depending on the sentence position in a text. Furthermore, the proposed method boosts not only the computational time and speed but it still comprehends the substantial information of a document. The experimental results suggest the proposed method well outperforms the baseline approaches significantly in both keyword extraction and summary sentence extraction.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Htet Myet Lynn, Eunji Lee, Chang Choi, Pankoo Kim,